High volume manufacturing requires efficient utilization of all manufacturing resources. This entails smart systems that easily adapt to changes in a manufacturing line due to a variety of factors including tool down situations, high WIP requirements and quality concerns.
On occasion, a production line experiences what is termed a Work In Process (WIP) bubble, i.e., an imbalanced distribution of the work in process, as a result of a variety of reasons which are difficult to predict. These include, but are not limited to, unscheduled tool down conditions, recipe inhibits and processing abnormalities affecting rework. Due to the dynamic environment of the production line, an automated system is needed to intelligently manage WIP bubbles when disruptions occur.
In most manufacturing lines, the product is evaluated by performing specific quality measurements following certain production processes. Measurements that quantify the aforementioned process are referred to as metrology. For certain processes, only a small sample of the product is subject to a metrology measurement. In others, all the products are measured depending on area specific parameters of the process in question. By modifying the sample rates of the metrology measurements, it is possible to quickly address and react to WIP bubbles, reducing their impact on the manufacturing line.
In a semiconductor manufacturing environment, metrology sampling rates are established for various process operations. The sampling rates may fluctuate depending upon a variety of factors, such as the criticality of the particular process, e.g., gate etch processes, stability of the process operations in terms of controllability, and the like. Metrology sampling rates are typically set below a level where the aggregate of all of the products selected for sampling will completely utilize all the available metrology capacity. This is generally referred to as the baseline sampling rates. Baseline sampling rates are advantageously set lower than maximum levels to allow the metrology tools to “catch-up” to accumulated WIP after one or more of the metrology tools have been taken out of service for a variety of reasons, such as routine maintenance, unscheduled problems with one of the metrology tools, etc. For example, if one of four available metrology tools are taken out of service, the work-in-progress (WIP) will slowly accumulate in the metrology queues until the out-of-service metrology tool is returned to service. At that time, all the remaining available metrology tools will operate at higher than normal utilization rates until the queues are reduced to normal.
Generally, when one or more metrology tools are taken out of production, the sampling rates may be manually lowered to reduce the amount of WIP accumulating in the metrology queues. Under this scheme, when the out-of-service metrology tool(s) returns to production, the sampling rates return to their normally high levels.
Another technique, referred to as an adaptive sampling technology, is intended to improve the efficiency of the factory. The key to addressing this issue is the ability to strike a balance between measurement needs of automated control and factory monitoring systems and the needs of the factory management to make the most effective use of available factory resources.
“Adaptive sampling” refers to an efficient and effective use of metrology sampling capacity. The finite metrology capacity of the factory is allocated to those measurements that provide the best actionable data for the factory. Sampling plans have been in place for years, typically allocating higher sampling rates to some processes rather than others.
Measurements of processes with small process windows or those displaying a high correlation to end-of-line yield may be given higher percentages than other, less critical measurements. Operations having a high wafer-to-wafer variability may measure more wafers but a smaller percentage of lots. While these strategies are effective to provide data they are designed to collect, they have the fundamental problem of being static—and modern factories are anything but static. Thus, truly efficient sampling strategies must be able to adapt to the changing factory dynamics.
Responding to customer demands for new technologies or product distributions creates another need for adaptive sampling methodologies. As new products are introduced, sampling rates must be comparatively high (up to 100% of the lots, in some cases) to ensure that enough data is collected to accurately target and manufacture the product when it is run in volume. However, maintaining a high level of sampling when the product is in full production is often unfeasible due to limited metrology capacity. Likewise, it is also often unnecessary as the volume of material being processed can provide an adequate stream of data for monitoring and control.
This type of adaptation to product life cycles was previously done manually and, as such, was rife with inefficiencies. Frequently, adaptation occurred only when a new product high sampling rate caused capacity issues (with resulting cycle-time penalties). Sampling rates for products required to be different for various portions of the production line. Alternatively, a product in a declining volume mode at lot start could still be a dominant product at the back-end-of-line.
Accordingly, an adaptive sampling system coupled to the WIP management system is required that takes all of these factors into account and implements appropriate sampling strategies. In view of the ever changing manufacturing environment, a need remains in industry for a system and a method capable of achieving dynamic sampling and of automatically increasing the utilization of manufacturing resources in response to WIP requirements.